Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital Holography 1,2
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Emlyn J. Davies , Daniel Buscombe , George W. Graham , and W. Alex M. Nimmo-Smith
(1) School of Marine Science & Engineering, Plymouth University, UK. (2) Now at: Department of Environmental Technology, SINTEF: Materials & Chemistry, Trondheim, Norway. (3) United States Geological Survey, Flagstaff, AZ, USA. Holography can provide reliable estimates of particle size & concentration I
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By providing in-focus images of suspended marine particles, digital in-line holography has become a valuable tool in many areas of marine research. However, estimating particle size distributions and concentrations using holography is computationally demanding compared to traditional techniques based on optics and acoustics. We have developed methods for the automatic identification, focusing, segmentation, classification and sizing of particles in holograms These have been evaluated using data collected from a variety of holographic systems, and from a significant range of particle types, sizes and shapes.
Holograms are numerically reconstructed & particles identified Noise Removal in Raw Holograms Significantly Aids Particle Detection
Reliable differentiation between sand, bubbles & diatoms in the surf zone Manual vs. Automated: Diameter
Manual vs. Automated: Area
Example raw hologram containing particles (a), background image (b), and corrected image following background removal (c).
It allows in-focus imaging of particles throughout a volume Particles are automatically segmented & sized from slices through volume
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Sample volume geometry and interference pattern
Examples of volume distributions for sieved Basalt spheres which were analysed both automatically (red) and manually (green). Vertical black lines indicate the limits of the sieved ranges of each sample for the following sizes: 90µm-106µm (a-c); 125µm-150µm (d-f); 180µm-212µm (g-i); 250µm-300µm (j-l).
sand (a-d), bubbles (e-h) and diatoms (i-l), using sand (a-d), bubbles (e-h) and diatoms (i-l), using 1/5, 1, 2 and 5 second averages. 1/5, 1, 2 and 5 second averages.
Cumulative Distribution of RMS Errors Greatly Reduce with Averaging
Montages from bursts collected in the surf zone. A, B, and C are characterized by sand particles, bubbles and diatoms, respectively.
Design is flexible, & now there is a commericial system (LISST-HOLO) Particles can be automatically classified by type based on shape/texture Proportion of observations which fall within a given % error for sand, bubbles and diatoms, calculated over periods (1/5 to 5 seconds). The dashed lines in each correspond to 30%, 50% and 100% RMS error.
These computational advances make holography a viable alternative to optic/acoustic backscatter technology I
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(a) nose-to-nose, (b) streamlined / profiling, and Streamlined design made at Plymouth (c) combined systems. Scattering of laser light University, deployed next to a nose-to-nose interferes with the incident light, creating an LISST-HOLO (Sequoia Scientific) interference pattern.
Mail:
[email protected] (Emlyn)
[email protected] (Dan)
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a) particles from a single hologram; b) binarised Shape parameters used for automated image; c) binarised image with detected classification with data-points shaded according outlines; d) particles classified (’s’ is sand; ’b’ is to manual classifications. bubble; and ’d’ is diatom).
[email protected] (George)
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It is possible to reconstruct, segment, size and classify particles imaged by submersible holography, reliably and completely unsupervised. Automated classification was capable of successfully differentiating sand grains, bubbles and diatoms, based on particle shape alone. Being able to distinguish between sand and other objects improved sand volume-concentration estimates by an average of 46%. Holography is unique in being able to provide size, shape, concentration and type of particle.
[email protected] (Alex)
WWW: http://holoproc.marinephysics.org
